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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory drivers influencing responsible AI (including the EU AI Act and GDPR)
- The role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Techniques for identifying bias in model outputs
- Strategies for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Crafting prompts for safety and reliability
- Mitigating the risks associated with unsafe or harmful outputs
- Applying alignment techniques in enterprise applications
Content Filtering and Moderation
- Designing pipelines for content filtering
- Implementing robust moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Defining governance frameworks specific to Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditability
- Secure logging practices for AI systems
- Ensuring traceability of model decisions
- Mechanisms for audit readiness and reporting
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights from real-world governance failures
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- A solid understanding of AI and ML fundamentals
- Familiarity with compliance and governance concepts
- Experience with enterprise IT or model deployment environments
Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
